Summary of study ST001269

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000854. The data can be accessed directly via it's Project DOI: 10.21228/M8998T This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

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Study IDST001269
Study TitleExosomal lipids for classifying early and late stage non-small cell lung cancer
Study TypeBiomarker Discovery
Study SummaryLung cancer is the leading cause of cancer deaths in the United States. Patients with early stage lung cancer have the best prognosis with surgical removal of the tumor, but the disease is often asymptomatic until advanced disease develops, and there are no effective blood-based screening methods for early detection of lung cancer in at-risk populations. We have explored the lipid profiles of blood plasma exosomes using ultra high-resolution Fourier transform mass spectrometry (UHR-FTMS) for early detection of the prevalent non-small cell lung cancers (NSCLC). Exosomes are nanovehicles released by various cells and tumor tissues to elicit important biofunctions such as immune modulation and tumor development. Plasma exosomal lipid profiles were acquired from 39 normal and 91 NSCLC subjects (44 early stage and 47 late stage). We have applied two multivariate statistical methods, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) to classify the data. For the RF method, the Gini importance of the assigned lipids was calculated to select 16 lipids with top importance. Using the LASSO method, 7 features were selected based on a grouped LASSO penalty. The Area Under the Receiver Operating Characteristic curve for early and late stage cancer versus normal subjects using the selected lipid features was 0.85 and 0.88 for RF and 0.79 and 0.77 for LASSO, respectively. These results show the value of RF and LASSO for metabolomics data-based biomarker development, which provide robust an independent classifiers with sparse data sets. Application of LASSO and Random Forests identifies lipid features that successfully distinguish early stage lung cancer patient from healthy individuals.
Institute
University of Kentucky
DepartmentCenter for Environmental and Systems Biochemistry
Last NameThompson
First NamePatrick
Address789 South Limestone, Lexington, Kentucky, 40536, USA
Emailptth222@uky.edu, rick.higashi@uky.edu
Phone8592181027
Submit Date2019-10-17
Total Subjects95
Publicationshttps://doi.org/10.1016/j.aca.2018.02.051
Raw Data AvailableYes
Raw Data File Type(s).raw
Analysis Type DetailLC-MS
Release Date2019-10-11
Release Version1
Patrick Thompson Patrick Thompson
https://dx.doi.org/10.21228/M8998T
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Cancer Stage
SA09213817Dec15_34exoEarly
SA09213917Dec15_2P58(19)Early
SA09214017Dec15_35exoEarly
SA09214117Dec15_36exoEarly
SA09214217Dec15_P193Early
SA09214317Dec12_P97(48)Early
SA09214417Dec12_8P76Early
SA09214517Dec11_10P66(25)Early
SA09214617Dec08_P145Early
SA09214717Dec11_P128Early
SA09214817Dec11_UK012Early
SA09214917Dec15_P92(46)Early
SA09215017Dec12_P83Early
SA09215117Dec18_17P64(23)Early
SA09215217Dec22_24exoEarly
SA09215317Dec20_P123Early
SA09215417Dec22_5P73(31)Early
SA09215517Dec22_P81Early
SA09215617Dec22_P89Early
SA09215717Dec20_P103Early
SA09215817Dec19_P82Early
SA09215917Dec18_2P147Early
SA09216017Dec08_P134Early
SA09216117Dec18_50exoEarly
SA09216217Dec18_P132(61)Early
SA09216317Dec19_21exoEarly
SA09216417Dec18_10exoEarly
SA09216517Dec18_UK009Early
SA09216617Dec08_P125Early
SA09216717Dec15_51exoLate
SA09216817Dec15_43exoLate
SA09216917Dec18_25exoLate
SA09217017Dec18_33exoLate
SA09217117Dec18_3exoLate
SA09217217Dec15_40exoLate
SA09217317Dec18_28exoLate
SA09217417Dec15_39exoLate
SA09217517Dec12_4exoLate
SA09217617Dec12_17exoLate
SA09217717Dec11_48exoLate
SA09217817Dec12_5exoLate
SA09217917Dec15_13exoLate
SA09218017Dec15_2exoLate
SA09218117Dec15_23exoLate
SA09218217Dec18_44exoLate
SA09218317Dec18_49exoLate
SA09218417Dec22_15exoLate
SA09218517Dec22_14exoLate
SA09218617Dec22_29exoLate
SA09218717Dec22_42exoLate
SA09218817Dec22_8exoLate
SA09218917Dec22_7exoLate
SA09219017Dec22_11exoLate
SA09219117Dec20_47exoLate
SA09219217Dec19_1exoLate
SA09219317Dec19_15P169(28)Late
SA09219417Dec19_32exoLate
SA09219517Dec19_41exoLate
SA09219617Dec20_45exoLate
SA09219717Dec20_30exoLate
SA09219817Dec18_6exoLate
SA09219917Dec11_27exoLate
SA09220017Dec08_26exoLate
SA09220117Dec11_22exoLate
SA09220217Dec08_9exoLate
SA09220317Dec08_19exoLate
SA09220417Dec11_18exoLate
SA09220517Dec08_P31Late
SA09220617Dec20_P109N28Normal
SA09220717Dec20_P95N19Normal
SA09220817Dec19_P37N12Normal
SA09220917Dec19_P192N49Normal
SA09221017Dec20_UK001NNormal
SA09221117Dec18_P28N5Normal
SA09221217Dec22_P32N7Normal
SA09221317Dec22_P91N17Normal
SA09221417Dec08_P90Normal
SA09221517Dec22_P35N10Normal
SA09221617Dec18_P162N42Normal
SA09221717Dec22_P209N50Normal
SA09221817Dec22_P135N37Normal
SA09221917Dec15_P36N11Normal
SA09222017Dec12_P108N27Normal
SA09222117Dec12_P94N18Normal
SA09222217Dec11_P99N21Normal
SA09222317Dec11_P88N15Normal
SA09222417Dec08_P98Normal
SA09222517Dec15_P104N24Normal
SA09222617Dec15_P126N33Normal
SA09222717Dec18_P101N22Normal
SA09222817Dec18_P127N34Normal
SA09222917Dec15_P29N6Normal
SA09223017Dec08_P110N29Normal
SA09223117Dec15_P164N44Normal
SA09223217Dec18_P136N38Normal
Showing results 1 to 95 of 95
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